In this screenshot of Central Silicon Valley, Census tracts have been combined with a constraints layer, and then cut with a 150 meter grid in the EPSG:3310 projection. Using imputation tables and external sources, each grid cell is then computed. The result is a statistically defensible, higher-resolution and handily applicable set of grid cells.

A systematic way to choose, extract and visualize data from the massive American Community Survey 5 Year census product is a challenge. I have written python code to ingest raw inputs into tables, and a small relational engine to handle the verbose naming.

An extraction and visualization process is underway… something like the following:

0) bulk tables in all geographies for all states
1a) define a batch of tables to extract by table_id
1b) choose a state or territory
1c) choose a geographic summary level

Once the choice is made, SQL + Python is executed, either as a standalone program in Linux or in the IPython Notebook. The code creates a working schema in PostgreSQL, copies table subsets into the new schema, and JOINs them with TIGER geometry to get spatial data. A preliminary, working version looks something like this:

In preparing for an upcoming Datathon, a column of data in PostgreSQL numeric format needed formatting for presentation. “Intersection Count” intersection_density_sqkm is a count of street intersections per unit area – a quick way to measure density of the built environment. A table of grid cells (covering the nine-county San Francisco Bay Area) that the column comes from consists of roughly 814,000 cells. How to quickly characterize the data contents? Use SQL and the psql quantile extension to look at ranges, with and without the zeroes.

So, recall that a natural log e of 1.0 is 0; a natural log of 116 is slightly over 4.75; a natural log of a number less than 1 is a negative number. To simplify the range for visualization, add a float column called data, set the new data column to the natural log of (intersection_density_sqkm + 1); use a simple multiply-then-divide technique to limit the precision to two digits (screenshot from an IPython Notebook session using psycopg2).

From: Jerome Villeneuve Larouche \To: "ubuntu@lists.osgeo.org" \Subject: [Ubuntu] DebianGIS Repo
Hello everyone,
This is a small message to tell you that the DebianGIS repo for Debian Wheezy is up and that every package on it is up-to-date! Everything is built against stable so you don't need to add any unstable
repo to use the latest GIS packages. To add it on your machine, edit "/etc/apt/sources.list" and add the line
deb http://debian.mapgears.com/repos/apt/debian/ wheezy main
You should also add the public key here
http://debian.mapgears.com/repos/apt/debian/debgis.gpg.key
Enjoy, as always if you have any questions about the repo, be it this
one or UbuntuGIS, send message on the mailing list!
PS: I'm also currently updating UbuntuGIS to Saucy!

Dozens of major news outlets posted articles yesterday profiling a paper published in the journal ‘Science’ by a team led by Matthew Hansen, a remote sensing scientist at the University of Maryland, along with extensive data.

A colleague pointed out the National Land Cover Database (NLCD) imagery today, which is not new, but it is useful. Here is a simple treatment of the San Francisco Bay Area, with city center markers matching the red urban coloring used in the base map. Click for the larger image, and you can see Lake Tahoe in the East and unmarked is Yosemite National Park almost due south. (’06’ in the blog post title refers to both the publication year of this base map, 2006, and the FIPS code for the State of California)

Two hours of condensed lecture was just enough time to cover the basics of the breadth of the Census, and the Instructor Dr. Jon Stiles did an expert job.

Following up on some leads from the class, I looked into “points of interest in the 2013 Census data versus OpenStreetMap.” The Census includes a table called pointlm. In the case of California, the file I looked at is called tl_2013_06_pointlm.shp. It is a simple layout, with the state code, an ANSI code, point ID, fullname and something called mtfcc. Rather than dig through 1000 pages of census docs, on a site that is out of service, I found this table , which has an easy to read format and a pointer to the definitions. A quick summary in SQL shows: